Criterica Intelligence — 23,706 production models trained on 475M+ real court records
Case Study · Deidentified

What the audit found in a $70M book.

A national pre-settlement litigation finance fund engaged Criterica to run a Portfolio Intelligence Audit against its active book. We scored roughly 8,400 cases with 23,706 production models trained on 475M+ real court records. What follows is the finding, with the firm and every counterparty anonymized and figures stated in bands.

"The fund did not have a returns problem. It had a visibility problem. Every finding below was already inside the portfolio. The audit made it legible, and put a number on what better intake scoring, pricing, and monitoring would have changed."

The engagement
~$70M
Active book audited
~8,400
Active cases scanned
~3x
Blended MOIC (reported)
~44%
Of book carried no risk score
The findings

Five things the fund did not know about its own book.

01
Operational system gaps

A reported return that the data did not support

The fund's case-management system reset each firm's lifetime funded amount to zero when a case closed. It tracked outstanding principal, not capital deployed. The largest relationship therefore appeared to return above 4x. Rebuilt against lifetime capital, the real figure was closer to 1.6x. The reporting was not wrong on purpose. The data architecture made the overstatement invisible.

02
Concentration & performance bands

The best returns sat in the smallest positions

The largest firm relationship, roughly $100M deployed over the life of the program, returned in the mid-1x range. The highest-returning relationship was a sub-$300K position compounding above 10x. Capital was concentrated where returns were lowest. The audit named the inversion the fund could not see from its own dashboards.

03
Monitoring failures

Nearly half the book could not be screened

About 44% of the active book, on the order of $31M, carried no risk classification and no expected-recovery amount. Those positions could not be scored, monitored, or stress-tested. The single largest source of unmanaged variance was not a bad case. It was a missing field at intake.

04
Capital velocity

Idle capital was quietly costing return

Settlements were redeploying with roughly a 45-day lag, leaving on the order of $9M sitting idle at any moment. That drag translated to approximately $325K per quarter in foregone return. Tightening the redeployment cycle toward 15 days recovered six figures annually, without writing a single new case.

05
Early warning

Deteriorating relationships were visible early

Firm-level outcome signals flagged do-not-use relationships 6 to 18 months before a manual review would have caught them. Two relationships already showing negative expected return were still receiving capital. Early warning is the difference between a managed exit and a write-down.

On variance

The audit does not claim every variance it surfaced was an avoidable loss. Some of those matters were still profitable. The value was identifying which variances were foreseeable through better intake scoring, pricing, duration modeling, concentration controls, and monitoring, and quantifying what catching them earlier would have been worth.

From audit to monitoring

The one-time finding became a live system.

After the audit, the same models run continuously against the active book. What was a snapshot becomes an alerting layer.

01Live MOIC and IRR tracking by firm, case type, and vintage
02Settlement and redeployment forecasting across the active book
03Do-not-use early warning on deteriorating firm relationships
04Underwater-advance screening against jurisdiction medians
05Capital allocation intelligence and concentration thresholds
06Docket and outcome monitoring on every active matter
See what the audit finds in your book.
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This case study is published with the client's identity and all counterparties removed. Financial figures are stated in approximate bands rather than exact values. It is presented to illustrate the Portfolio Intelligence Audit methodology, not as a forward-looking projection of returns.